Exploring the influential reviewer, review and product determinants for review helpfulness

Artificial Intelligence Review. 2020. Vol. 53. No. 1. P. 407–427.

M. S. I. Malik, Hussain A.

Helpfulness of online reviews is a multi-faceted concept. The reviews are usually ranked on
the basis of perceived helpful votes and aid in making purchase decisions for online customers.
This study extends the prior work done for review helpfulness by considering not
only the influential characteristics of reviews but also incorporates influential indicators of
reviewer and product category. Influential factor based new features (product, reviewer and
review) are proposed to predict the helpfulness of online reviews by using five ML methods.
The experimental analysis on a real-life review dataset shows that the hybrid set of
proposed features deliver the best predictive performance. In addition, the reviewer and the
reviewcategory features introduced in this research exhibit better predictive performance as a
standalone model. Findings show that reviews which have large number of comments, large
values of sentiment and polarity scores receive more helpful votes. The reviewer activity
length and recency are statistically significant predictors for helpfulness prediction. In addition,
number of question answered, ratio of positive reviews and average rating per review
are also significant variables of product type. The findings of this study highlight the number
of implications for research and provide new insights to retailers for efficient ranking and
organization of consumer reviews for online users.

Keywords: machine learning, Review helpfulness, Neural networks, Reviewer characteristics